Guided CNN for generalized zero-shot and open-set recognition using visual and semantic prototypes.

•Decomposing G-ZSL task into an OSR task and a ZSL task while jointly.•training the models of these two tasks effectively addresses the CO problem which is ubiquitous in most existing G-ZSL methods.•Introducing the accumulated side information from known classes to OSR to first explore a new generalized open set recognition (G OSR) task.•A visual and semantic prototypes jointly guided convolutional ne ural net work (VSG CNN) is proposed to fulfill these two tasks (G ZSL and G OSR) in a unified end to end learning framework.•Extensive experiments on benchmark datasets indicate the validity of our VSG-CNN.